Challenges in Machine Learning Workshop at NIPS

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Isabelle Guyon

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Aug 8, 2016, 5:54:53 PM8/8/16
to Active Learning (Machine Learning)
Challenges in Machine Learning
Gaming and Education
NIPS 2016 workshop proposal
Friday December 9, 2016, Barcelona, Spain

http://ciml.chalearn.org/

Call for abstract: 
We welcome 2-page extended abstracts on topics relating to challenges in machine learning and gaming in education at large. Selected papers will be presented primarily as posters, but exceptional contributions will be given oral presentations. Abstract should be submitted by October 10th, 2016 by sending email to nips...@chalearn.org.

Motivations

Challenges in machine learning and data science are competitions running over several weeks or months to resolve problems using provided datasets or simulated environments. The playful nature of challenges naturally attracts students, making challenge a great teaching resource. For this third edition of the CiML workshop at NIPS we want to explore more in depth the opportunities that challenges offer as teaching tools. The workshop will give a large part to discussions around several axes: (1) benefits and limitations of challenges to give students problem-solving skills and teach them best practices in machine learning; (2) challenges and continuous education and up-skilling in the enterprise; (3) design issues to make challenges more effective teaching aids; (3) curricula involving students in challenge design as a means of educating them about rigorous experimental design, reproducible research, and project leadership.
CiML is a forum that
 brings together workshop organizers, platform providers, and participants to discuss best practices in challenge organization and new methods and application opportunities to design high impact challenges. Following the success of last year's workshop, in which a fruitful exchange led to many innovations, we propose to reconvene and discuss new opportunities for challenges in education, one of the hottest topics identified in last year's discussions. We have invited prominent speakers in this field. 
We will also reserve time to an open discussion to dig into other topic including open innovation, coopetitions, platform interoperability, and tool mutualisation.



Invited speakers

Emma

Emma Brunskill
(CMU)
Learning to improve learning: ML in the classroom
sebastien

Sebastien Marcel
(IDIAP)
Reproducible Research: teaching scientific method
henning

Henning Muller
(MediaEval, ImageClef)
Evaluation-as-a-Service: 
a serious game
joaquin

Joaquin Vanschoren 
(TU Eindhoven)
OpenML in research and education
 

Larry Zitnick 
(MSCOCO) 
Gathering common sense knowledge: how to game it?
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